wehub-resource-sync
e768098d0e
Flake8 Lint / flake8 (push) Waiting to run
Publish Promptflow Doc / Build (push) Waiting to run
Publish Promptflow Doc / Deploy (push) Blocked by required conditions
Spell check CI / Spell_Check (push) Waiting to run
tools_continuous_delivery / Private PyPI non-main branch release (push) Has been skipped
tools_continuous_delivery / Private PyPI main branch release (push) Failing after 2m42s
71 lines
2.5 KiB
Markdown
71 lines
2.5 KiB
Markdown
# Basic Chat
|
||
This example shows how to create a basic chat flow. It demonstrates how to create a chatbot that can remember previous interactions and use the conversation history to generate next message.
|
||
|
||
Tools used in this flow：
|
||
- `llm` tool
|
||
|
||
## Prerequisites
|
||
|
||
Install promptflow sdk and other dependencies in this folder:
|
||
```bash
|
||
pip install -r requirements.txt
|
||
```
|
||
|
||
## What you will learn
|
||
|
||
In this flow, you will learn
|
||
- how to compose a chat flow.
|
||
- prompt template format of LLM tool chat api. Message delimiter is a separate line containing "#", role name and colon: "# system:", "# user:", "# assistant:".
|
||
See <a href="https://platform.openai.com/docs/api-reference/chat/create#chat/create-role" target="_blank">OpenAI Chat</a> for more about message role.
|
||
```jinja
|
||
# system:
|
||
You are a chatbot having a conversation with a human.
|
||
|
||
# user:
|
||
{{question}}
|
||
```
|
||
- how to consume chat history in prompt.
|
||
```jinja
|
||
{% for item in chat_history %}
|
||
# user:
|
||
{{item.inputs.question}}
|
||
# assistant:
|
||
{{item.outputs.answer}}
|
||
{% endfor %}
|
||
```
|
||
|
||
## Getting started
|
||
|
||
### 1 Create connection for LLM tool to use
|
||
Go to "Prompt flow" "Connections" tab. Click on "Create" button, select one of LLM tool supported connection types and fill in the configurations.
|
||
|
||
Currently, there are two connection types supported by LLM tool: "AzureOpenAI" and "OpenAI". If you want to use "AzureOpenAI" connection type, you need to create an Azure OpenAI service first. Please refer to [Azure OpenAI Service](https://azure.microsoft.com/en-us/products/cognitive-services/openai-service/) for more details. If you want to use "OpenAI" connection type, you need to create an OpenAI account first. Please refer to [OpenAI](https://platform.openai.com/) for more details.
|
||
|
||
```bash
|
||
# Override keys with --set to avoid yaml file changes
|
||
pf connection create --file ../../../connections/azure_openai.yml --set api_key=<your_api_key> api_base=<your_api_base> --name open_ai_connection
|
||
```
|
||
|
||
Note in [flow.dag.yaml](flow.dag.yaml) we are using connection named `open_ai_connection`.
|
||
```bash
|
||
# show registered connection
|
||
pf connection show --name open_ai_connection
|
||
```
|
||
|
||
### 2 Start chatting
|
||
|
||
```bash
|
||
# run chat flow with default question in flow.dag.yaml
|
||
pf flow test --flow .
|
||
|
||
# run chat flow with new question
|
||
pf flow test --flow . --inputs question="What's Azure Machine Learning?"
|
||
|
||
# start a interactive chat session in CLI
|
||
pf flow test --flow . --interactive
|
||
|
||
# start a interactive chat session in CLI with verbose info
|
||
pf flow test --flow . --interactive --verbose
|
||
```
|
||
|